Deep learning (DL) has recently changed the development of intelligent systems and is
widely adopted in many real-life applications. Despite their various benefits and potentials …

This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

Abstract You Only Look Once (YOLO), known for its real-time performance and outstanding
accuracy, has emerged as a prominent framework for object detection tasks. However …

With the introduction of spectrum sharing and heterogeneous services in next-generation
networks, the base stations need to sense the wideband spectrum and identify the spectrum …

A visionary computing paradigm is to train resource efficient neural networks on the edge
using dedicated low-power accelerators instead of cloud infrastructures, eliminating …

The most compute-intensive stage of deep neural network (DNN) training is matrix
multiplication where the multiply-accumulate (MAC) operator is key. To reduce training …

The coming 6G wireless network is poised to achieve unprecedented data rates, latency,
and integration with newer technologies like AI and IoE. On the other hand, along with this …

Deep learning is computationally intensive, with significant efforts focused on reducing
arithmetic complexity, particularly regarding energy consumption dominated by data …

Reducing the memory footprint of Machine Learning (ML) models, particularly Deep Neural
Networks (DNNs), is essential to enable their deployment into resource-constrained tiny …

Vision-based hand gesture recognition in human-computer interface design has useful
applications in virtual-reality, gaming control, communication through sign language …